A no-show and a logbook problem are different people

The candidate who never made orientation and the driver whose logs stopped matching the trips both cost you a week. They are not the same hire failing twice. In the review data they almost never share a paragraph, and a screen built for one will not catch the other.

Among the reviews carriers left on CDLScan, 5.3% mention a no-show. That figure describes those reviews, not drivers in general: carriers write after a bad run, so the share tells you what shows up once someone bothers to report it.

Inside that slice, only 1.0% also mention logbook and hours problems. If the two events were independent, you would expect about 4.6% — the logbook share of all reviews. The observed overlap is about a fifth of chance. Lift is 0.22. Flip the pair and 1.2% of logbook reviews also mention a no-show. Same thin overlap, two bases. Rare together is not the same as impossible. It is a strong hint you are looking at two populations.

Two failures, two clocks

A no-show is an onboarding loss. You spent a ticket, a hotel, a recruiter’s week. You did not hand over a unit. The language is never showed, didn’t show, flight ticket, plane ticket, ticket never, booked flight, showed orientation.

Logbook and hours problems are an operations loss. The driver already arrived, already took a truck, and already had an ELD in front of them. The language is log book, disconnected ELD, ELD device, HOS rules, use ELD, log books. That is a person who sat in the seat and then could not, or would not, keep the hours honest.

Those clocks do not usually share a review because they do not usually share a person. The driver who never arrives cannot disconnect your ELD. The driver with a logbook problem already showed up.

What actually rides with a no-show is the empty-chair cluster. Drugs or alcohol sit in 31.7% of no-show reviews. Paid travel with no arrival sits in 30.8%. Going unreachable sits in 13.0%. An abandoned truck sits in only 7.8% — against a 33.5% base rate, far below chance. Theft sits in 5.7%. False statements sit in 5.0%. Late deliveries sit in 4.3%. Logbook problems are not on that list.

What actually rides with logbook and hours problems is the on-the-job cluster. An abandoned truck sits in 36.2% of those reviews. Aggression sits in 22.3%. A crash or damage sits in 21.3%. Late or missed deliveries sit in 17.6%. Theft sits in 15.3%. Going unreachable sits in 9.5%. Equipment or a fuel card never returned sits in 7.4%. A no-show is not on that list.

Stay away and waste your time are a different register. In 39.6% of no-show reviews the carrier also writes a verdict. In 32.7% of logbook reviews they do the same. That is a fact about how these reviews end, not a fact about the driver. Read the scene. Do not hire off the temperature of the last sentence.

The pairing that does sit together on the onboarding side is paid travel and a no-show: 30.8% of no-show reviews also mention paid travel, about five times chance. That is unpacked in paid travel and a no-show. No-shows as their own category are in no-show driver reviews. The hours side is in logbook and hours reviews and in late loads and logbook hours. The broader split between the empty chair and the walk-off is in why no-shows and abandoned trucks barely overlap.

How we count these shares, and why year totals cannot be compared, is on the methodology page.

Why recruiters mash them together

Because both waste a week and both look like “they didn’t work out.” Because both are mostly invisible on a first-pass packet — a no-show is unfiled, and a logbook fight often stays in a review instead of a violation. Because a desperate desk wants one screen that catches every early loss.

One screen will not. The application before a no-show is urgency plus a thin last job plus a quiet packet. The application before a logbook problem is a completed stint, a previous carrier who already used log-book or disconnected-ELD language, and often a late-delivery or crash companion. If you only hunt for never-showed, you will hire the person who is pleasant at orientation and then cannot keep the hours. If you only hunt for the ELD, you will buy flights for people who have already done the empty-chair version to someone else.

The share of reviews mentioning a no-show ran 3.7% in 2018, 3.3% in 2019, 3.8% in 2020, 5.1% in 2021, 5.8% in 2022, 5.0% in 2023, 6.2% in 2024, and 4.9% in 2025. I stop that line at 2025. 2026 is still open.

Logbook mentions ran 5.0% in 2018, 5.8% in 2019, 6.5% in 2020, 5.5% in 2021, 5.5% in 2022, 4.6% in 2023, 4.3% in 2024, and 3.7% in 2025. One line rose, one fell. They still do not sit in the same write-up.

What official reports see — and what they don’t

This pair splits the packet the other way from a crash. Hours violations can be a filing. A no-show is not. PSP, an MVR, and the Clearinghouse were built to hold inspections, crashes, license actions, program events. They were not built to hold an unused flight ticket.

That split is useful and it is also a trap. A clean packet on a logbook-heavy history is a real finding — it means the filed items are clean, or the hours fight never rose to a violation. A clean packet on a no-show history is the default. It tells you nothing. Do not let the fact that one of these two can show up on an inspection make you treat the other as covered.

Previous-employer forms help the hours side more than the empty chair, and they still fail often. The last desk may confirm dates and leave the log-book story in a review instead of a form. For the never-starts, look at previous tickets and empty orientation days. For the hours files, look at previous ELD language, disconnected-device notes, and whether anyone called it in.

Drugs or alcohol are the exception that proves the split. They show up in 31.7% of no-show reviews. The official system that can catch a test event is pointed at the onboarding population more than at the logbook write-up. Do not let that make you treat every hours problem as a drug article. The data says the opposite.

I am not going to pretend this is an independent study. CDLScan is the source, and I write from the data they share. A no-show is common enough in what carriers bother to write down, a logbook problem almost never rides along, and the packet you already run is loud on one and silent on the other.

What to do with two screens

Run two short checks, not one vague “will they stay.”

Before you buy a ticket or lock orientation: search the name for never showed, didn’t show, flight ticket, paid travel. Ask when they can start if the flight waits. That screen is for the empty chair.

Before you put them on a live dispatch: search the name for log book, disconnected ELD, HOS rules. Ask whether the last carrier had hours trouble, and whether the device stayed connected. That screen is for the person who already sat in the seat.

If either history is already written down, stop and read it. Check the name on CDLScan before the spend. It takes less than a minute, and it is where both populations actually sit — usually in different write-ups.

Frequently asked questions

Does a 1.0% overlap mean a no-show never later has hours trouble?

No. Rare together is not impossible. It means that in the reviews carriers left on CDLScan, a no-show and a logbook problem almost never share a paragraph. Do not run one screen and assume you covered both.

Why use lift instead of just the 1.0%?

Because 1.0% needs a baseline. Logbook and hours problems are 4.6% of all reviews, so chance would put them in about 4.6% of no-show reviews. 1.0% is about a fifth of that. Lift 0.22 is the short way to say “far below chance.”

Why is the other share 1.2%?

Because the base changed. 1.2% of logbook reviews also mention a no-show. Same overlap, counted from the other side. Always name the denominator.

Which failure is more common in the reviews?

A no-show is in 5.3% of the reviews. Logbook and hours problems are in 4.6%. They are similar in size. They are not the same people.

Will a clean PSP tell me which type I have?

It can speak to the hours side if something was filed. It will not speak to the no-show. A clean packet means the filed items are clean. It does not classify the next empty chair.

What should I ask on the reference call?

For a thin onboarding history: did you buy them a ticket, and did they arrive. For a completed stint with hours language: did the ELD stay connected, and were the logs matching the trips. Those are different questions.

Where do I look the name up?

On CDLScan, before you buy the ticket or put them on a live load. The two populations are already written down. They are usually not written down together.